Content providers may know everything, learning platforms may teach everything, assessors may confirm learners learned what was taught, and credential-ers (both traditional and digital like Parchment and YourAcclaim.com) may communicate that learning. But keep talking... it is LinkedIn's skill-to-job, and employee-employer ecosystem in the end.

This was recently hammered home in a talk given by Dan Shapero - LinkedIn’s head of career products. LinkedIn’s mission is pretty clear. But for those folks still scratching their heads over endorsements, or thinking the LinkedIn mission is just about being the best networking database, let me enlighten.

LinkedIn wants to create the world’s *economic graph* - where they link every person, every company, and every piece of professional knowledge. They believe access to the best talent is the main catalyst for economic growth, and they intend to help the individual grow their career. Absorb all the data, interpret it, define the relationships.

Two recent examples where LinkedIn is ‘absorbing all the data’ include their Add to Profile capability for educational achievements andAdd to Profile for certifications.

LinkedIn is in a renewed push for data acquisition, data they hope to use to make money by helping individuals grow their skills, and by helping companies find those skilled individuals. What could they do with this data?

Consider credentials and credential valuation. There are two related outcomes LinkedIn is capable of driving. First essentially, anything and everything can be a credential. And second, the dollar value of a credential in the economic graph CAN BE KNOWN. In the past, recognized brands following established standards for achievement created credentials. The University of Minnesota gave an A for a Project Management class, the State of Minnesota trade regulatory board issued a credential linked to apprenticeship, an IT company like Microsoft or Cisco gave a professional credential for passing a series of tests. One of the barriers to entry for alternative or smaller credentials, is how they obtain value in the marketplace if they aren’t known.

LinkedIn plans to determine that value, and in doing so not only tell you what you stand to gain economically by investing time and money in that IT certification, but also providing economic feedback on a whole host of other ‘non-traditional’ credentials you could earn. My guess is that they only have to obtain salary data to start drawing interesting conclusions about how credentials of all kinds relate to career growth and opportunity, and therefore the market value of that credential. Credentials could be anything, and everything, and the most valuable ones will win. LinkedIn is Electricity.

LinkedIn doesn’t believe credentials alone will define the individual. Starting at 1:07, Dan provides his five-year look into the future, which could be interpreted as a LinkedIn roadmap. Among the more interesting ideas…

Reputation matters. Credentials (again, of all kinds) won't be the only factor - reputation will be a key component. LinkedIn is interested in online reputation and will also improve it's own understanding of your reputation. Endorsed for iOS skills? Great. But endorsed by a recognized expert in iOS, even better. Dan proposed several ways in which this could work, so while we don't know how they plan to implement, they believe it will be a key factor. Their professional network graph and endorsements gives them a huge start.

Proof-of-work will count for something. Portfolios, writing samples, work examples that can be shared and discussed... these will form another part of the employability picture for an individual. “I could tell you about my achievements, or I could show you what I can do as well.”

Dan Shapero, LinkedIn's Head of Career Products, gave the opening keynote two weeks ago at ATP. ATP is the largest testing industry conference for professional assessment and certification. The keynote was provocative, challenging, and very interesting.

I think the keynote says a lot about how LinkedIn views itself, and where it's going. I'll digest in a future post, but folks wanting to review the source material can find it online here.

Skip to 40:24 to avoid ATP administrivia and jump right into the kick-off.

Last year, I spent some time with one of our largest customers talking about Big Data. Leaders there have deep ties to statistical analysis and research methods. They were after all, part of the psychometric old guard that had invented and refined some of the most advanced aspects of high-stakes testing.

Big Data hype would lead you to believe it would cure cancer and create world peace. They were not falling for it.

At issue was the idea that your data could tell you something. 'Data doesn’t tell you anything, you have to ask it questions, you have to have a theory you are testing.’ I don't disagree with this science and statistical fundamental, and I certainly feel that the hype cycle on big data is nearing historical proportions.

One thing we agreed on was that often data can be explored to develop interesting questions to ask. The recent "Ballghazi” scandal involving under-inflated footballs provides an interesting example of how this might happen.

Football data analyst Warren Sharp delivered a thought-provoking analysis of NFL Patriots performance on one game aspect that might have relation to football inflation: fumble turnover prevention. In a nutshell, the Patriots are better at this game statistic than any other team. But not just better as in top-of-the-bell-curve, better as in ‘off-the-bell-curve', literally off-the-chart.

Take a few minutes to read, and you’ll not only be treated to an interesting spelunking of NFL data, but you can see how data can be explored to identify interesting topics to further theorize about. Develop a theory for WHY the patriots are so good at this statistic, and you can go and test that. Against the data.

UPDATE: There is a detailed critique of the original data analysis here. Further reminders about data interpretation, and not over-reaching on theories.